Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

Confidence intervals for secondary parameters following a sequential test

In sequential studies, formal interim analyses are usually restricted to a consideration of a single null hypothesis concerning a single parameter of interest. Valid frequentist methods of hypothesis testing and of point and interval estimation for the primary parameter have already been devised for use at the end of such a study. However, the completed data set may warrant a more detailed analysis, involving the estimation of parameters corresponding to effects that were not used to determine when to stop, and yet correlated with those that were. This paper describes methods for setting confidence intervals for secondary parameters in a way which provides the correct coverage probability in repeated frequentist realizations of the sequential design used. The method assumes that information accumulates on the primary and secondary parameters at proportional rates. This requirement will be valid in many potential applications, but only in limited situations in survival analysis.     

The Informal Sector: A Review and Agenda for Management Research

Despite its connotations of non‐compliance, illegality, social exploitation and marginality, the informal sector is a substantial contributor to economic life in developing countries and, increasingly, in more technologically advanced activities. Its prevalence in developed economies has also become more widely recognized. In light of its significance, this paper reviews research on the informal sector from a management and organization scholarship perspective, rather than from an entrepreneurship view, as has been the focus until now. It sets out the atypical management practices that are inherent in the sector, explores the under‐researched relationship between formal and informal firms, and highlights definitional, conceptual and other limitations in extant research. As a step in resolving these issues, the authors present a conceptual model of formality and informality in a three‐dimensional framework that highlights an organizational infrastructure dimension, a view of firms operating along a continuum, and a multi‐level analytical context. Building on this, the authors detail opportunities for enhanced appreciation of in situ management and organizational practices in the informal sector and outline tools for pursuing a management and organization scholarship agenda. Overall, the authors argue that management scholarship has great potential to improve understanding of the informal sector, and that the informal sector provides opportunities to advance management theory, research and practice.     

Planners’ Perceptions of Why Canadian Communities Have Too Many Plans

A survey of Canadian planners explored perceptions about factors contributing to the growing number of plans communities have adopted recently. The results suggest that practitioners see themselves as responding to current community concerns, as well as to requirements imposed by higher levels of government. Neoliberal practices that force compliance with accountability frameworks to enable transfer payments increase demands for new plans. Professional practices within planning have encouraged specialization—in fields such as urban design—that often leads to plan-making activities. With many plans to coordinate, and limited staff resources and time available, practitioners face increasing implementation challenges.     

Illuminating the Phenomenological Challenges of Cross-Cultural Supervision

ABSTRACT

The authors discuss transference enactments that occur during cross-cultural supervision. Previous research has shown that few mental health professionals willingly talk about race during supervision and that White supervisees experience significant difficulty when directly engaged in discussions about race. They introduce a new phenomenon, “stereotypical” transference enactments, as a means for understanding the countertransference Black female clinical supervisors experience during clinical supervision of White supervisees. The findings indicated that effective cross-cultural supervision should address the intersectionality of race and gender to enhance the development of multicultural clinical skills. The clinical and educational challenges are discussed by using two case vignettes.     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.     

Psychosocial Correlates of Interpersonal Sensitivity: A Meta-Analysis

This meta-analysis examines how interpersonal sensitivity (IS), defined as accurate judgment or recall of others’ behavior or appearance, is related to psychosocial characteristics of the perceiver, defined as personality traits, social and emotional functioning, life experiences, values, attitudes, and self-concept. For 215 independent studies reported in 96 published sources, higher IS was generally associated with favorable or adaptive psychosocial functioning. Significant mean correlations were found for 27 of the 40 categories of psychosocial variables; these categories covered many different personality traits, indicators of mental health, and social and work-related competencies. Moreover, many additional studies that fell outside these conceptual categories also showed significant positive relations between IS and numerous other psychosocial variables. Taken together, the results support the construct validity of IS tests and demonstrate that IS is associated with many important aspects of personal and social functioning.